An entire life-cycle rolling bearing remaining useful life prediction method using new degradation feature evaluation indicators

Abstract

The rolling bearing remaining useful life (RUL) prediction is a hot topic issue in the field of rail transportation. The existing RUL prediction methods for rolling bearing have problems such as unreasonable division of rolling bearing degradation stages and incomplete extraction of degradation features by feature selection indicators. In order to solve these problems, an entire life-cycle rolling bearing RUL prediction method using new degradation feature evaluation indicators is proposed. Firstly, the degradation feature evaluation indicator is designed to evaluate the stability of the degradation feature. Then, the combination of stability evaluation indicator and correlation evaluation indicator is used as the basis for feature selection. Secondly, the Gaussian Mixture Model (GMM) method is fused with the Support Vector Machine (SVM) to divide the bearing entire life-cycle into three stages: normal stage, early degradation stage, and degradation stage. Finally, the Long Short-Term Memory (LSTM) network model is trained separately to predict the rolling bearing RUL for different rolling bearing degradation stages. The effectiveness of the proposed prediction method based on different degradation stages of rolling bearing in predicting the RUL of rolling bearing is verified through PRONOSTIA bearing dataset. The comparison with existing methods shows that this approach demonstrates superior accuracy and predictive performance. For example, the Mean Square Error (MSE) evaluation metric has decreased by 60%. The Root Mean Square Error (RMSE) evaluation metric has decreased by 36.5%. The Mean Absolute Error (MAE) evaluation metric has decreased by 48.6%. The Mean Absolute Percentage Error (MAPE) evaluation metric has decreased by 36.9%.